MACE

Researchers introduced MACE, a Multi-Agent Candidate Event acquisition method that uses evidence-specialized LLM agents to refine event structure before linking, according to a new arXiv paper (arXiv:…

Researchers introduced MACE, a framework that selects complementary LLM partners to reduce cumulative regret in multi-agent coordination tasks. Experiments show that random pairing outperforms in-cont…

Researchers introduced CrystalGRPO, a reinforcement-learning post-training framework for flow-based crystal structure prediction that jointly optimizes coordinates and lattice parameters. In tests on …

Researchers introduced the Coarse-Fine Transport Distance (CFTD), a distribution-based evaluation framework using atom-averaged features from pretrained Machine-Learning Interatomic Potentials (MLIPs)…

A new research paper by Choi et al. (2026) finds that multi-agent LLM systems often fail to explore their teammates, sticking with one peer and missing better collaborators. The paper introduces MACE …

Researchers introduced Stein kernelized molecular dynamics (SKMD), an enhanced sampling method that uses interacting particle dynamics to acquire informative training configurations for active learnin…